基于BP-EKF算法的锂电池SOC联合估计
|
摘要
电池荷电状态(SOC)的准确估计是电动车辆进行整车控制优化的先决条件,也是合理实施电池管理的依据。本文中在确定1阶RC等效电路模型的基础上,采用含有遗忘因子的递推最小二乘算法和BP-EKF算法对模型参数与SOC进行在线联合估计,提出一种BP神经网络和扩展卡尔曼滤波(EKF)相结合的锂离子动力电池SOC估计方法,使用相应的滤波输出参数离线训练BP神经网络,进而将训练成功的BP神经网络用于补偿EKF算法的估计误差。通过仿真和电池动态工况试验验证,结果表明,与EKF算法相比,所提出的SOC估计方法具有良好的抑制发散和鲁棒性能,能有效提高SOC的估计精度。
Accurate estimation of battery SOC is not only a prerequisite for control and optimization of electric vehicle,but also the basis for the reasonable implementation of battery management. In this paper,based on the first-order RC equivalent circuit model,a SOC estimation method for lithium power battery is proposed by combining BP neural network with extended Kalman filtering(EKF),in which recursive least-squares with forgetting factor and BP-EKF algorithm are adopted to conduct a co-estimation of SOC and other model parameters. Corresponding filter output parameters are used to train BP neural network off-line,and succed trained BP neural network are used to compensate the estimation error in EKF algorithm. The results of simulation and dynamic state test show that compared with EKF algorithm,the SOC estimation method proposed has good performances in divergence suppression and robustness,and can effectively enhance the estimation accuracy of battery SOC.
Accurate estimation of battery SOC is not only a prerequisite for control and optimization of electric vehicle,but also the basis for the reasonable implementation of battery management. In this paper,based on the first-order RC equivalent circuit model,a SOC estimation method for lithium power battery is proposed by combining BP neural network with extended Kalman filtering(EKF),in which recursive least-squares with forgetting factor and BP-EKF algorithm are adopted to conduct a co-estimation of SOC and other model parameters. Corresponding filter output parameters are used to train BP neural network off-line,and succed trained BP neural network are used to compensate the estimation error in EKF algorithm. The results of simulation and dynamic state test show that compared with EKF algorithm,the SOC estimation method proposed has good performances in divergence suppression and robustness,and can effectively enhance the estimation accuracy of battery SOC.
引文
[1]华周发,李静.电动汽车动力电池SOC估算方法综述[J].电源技术,2013,37(9):1686-1689.
[2]XIONG Rui,SUN Fengchun,et al.A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles[J].Applied Energy,2013,63(6):1421-1433.
[3]PLETT G.Extended kalman filtering for battery management systems of Li PB-based HEV battery packs,Part 1,Background[J].J.Power Sour,2004,134(2):252-261.
[4]黄文华,韩晓东,陈全世,等.电动汽车SOC估计算法与电池管理系统的研究[J].汽车工程,2007,29(3):198-202.
[5]杨世春,麻翠娟.基于PNGV改进模型的SOC估计算法[J].汽车工程,2015,37(5):582-586.
[6]林成涛,陈全世,王军平,等.用改进的安时计量法估计电动汽车动力电池SOC[J].清华大学学报(自然科学版),2006,46(2):247-251.
[7]XIONG Rui,HE Hongwen,ZHAO Kai.Research on an online identification algorithm for a thevenin battery model by an experimental approach[J].International Journal of Green Energy,2015,12(3):272-278.
[8]HU Xiaosong,SUN Fengchun,et al.Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting[C].Proceedings of 2011 American Control Conference,San Francisco,California,2011:935-940.
[9]张利,朱雅俊,刘征宇.锂离子电池SOC与模型参数联合估算研究[J].电子测量与仪器学报,2012,26(4):320-324.
[10]VILA J P,WAGNER V,NEVEU P.Bayesian nonlinear model selection and neural network:A conjugate prior approach[J].IEEE Trans on Neural Networks,2000,11(2):265-278.
[2]XIONG Rui,SUN Fengchun,et al.A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles[J].Applied Energy,2013,63(6):1421-1433.
[3]PLETT G.Extended kalman filtering for battery management systems of Li PB-based HEV battery packs,Part 1,Background[J].J.Power Sour,2004,134(2):252-261.
[4]黄文华,韩晓东,陈全世,等.电动汽车SOC估计算法与电池管理系统的研究[J].汽车工程,2007,29(3):198-202.
[5]杨世春,麻翠娟.基于PNGV改进模型的SOC估计算法[J].汽车工程,2015,37(5):582-586.
[6]林成涛,陈全世,王军平,等.用改进的安时计量法估计电动汽车动力电池SOC[J].清华大学学报(自然科学版),2006,46(2):247-251.
[7]XIONG Rui,HE Hongwen,ZHAO Kai.Research on an online identification algorithm for a thevenin battery model by an experimental approach[J].International Journal of Green Energy,2015,12(3):272-278.
[8]HU Xiaosong,SUN Fengchun,et al.Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting[C].Proceedings of 2011 American Control Conference,San Francisco,California,2011:935-940.
[9]张利,朱雅俊,刘征宇.锂离子电池SOC与模型参数联合估算研究[J].电子测量与仪器学报,2012,26(4):320-324.
[10]VILA J P,WAGNER V,NEVEU P.Bayesian nonlinear model selection and neural network:A conjugate prior approach[J].IEEE Trans on Neural Networks,2000,11(2):265-278.